This course was helpful, but was too reliant on IBM tools. I realize this is an IBM course, but lots of students were unable to create a Watson account, including myself. Because of that, it was difficult to do any labs or assignments involving Watson. I requested help from the tech support e-mail and never got any help. From incomplete instructions, I had to figure out how to do my final assignment in Jupyter notebooks in Anaconda and submit through Git Hub. It took lots of extra time and I was worried I didn't do something correctly with Ananconda and Git Hub and wouldn't finish the course on time. Also, too many tools were presented in this course. Although it's helpful to know of the existence of these tools, simply describing the most common ones and letting us do a Google search to learn about others would have sufficed.

It was a pretty great course. I have learnt a lot about data science tools. However, the pacing is a little bit too fast. I feel like I am in rush, so many things to absorb for a small course. It would be better if you guys extend the course a bit longer with in depth explanation about the tools with practical labs and exercies instead of breifly explaining things within a video. Another reason i gave this a 3 stars was due to some out of date instructions & screenshots in lab exercies, especially in week 3, IBM watson studio exercies. The instructions are not clear and screenshots do not match with the current version of the studio. It took me a while to understand what the instructions want me to do. Other than that it was a great course. Hope you read my review and improve the course in future. Best of Luck....

This course is "information overload". Personally I think that the structure can do with some tweaking. The reason for this is because the information lacks context.

Week 1 and 2 were a bit daunting, but if you stick with it it does get better.

It would help if there was a resources tab with summaries of the content discussed in the videos. Also, when introducing the programming languages, they should say whether these languages are required. I spent a lot of time downloading, installing, and trying to load the required libraries to do the assignments. It was only later that I discovered that it is not necessary, and it is far quicker and more reliable to do these assignments in the free resources provided.

Overall, not as bad as the other students make out in the comments, reviews and discussions.

The course is not bad at all, on the contrary, it is very rich in information. However, a lot of the IBM additional Tools were presented very briefly, with no practical examples / labs, so I believe all that info will be forgotten very fast. The quizzes are often about memorization, while they could be more about understanding the principles behind what we're learning. For instance, a few pieces of code to be interpreted could be given, instead of the same old generic knowledge. Course is a bit too heavy on the theoretical side, but manageable nonetheless. Adding a few additional practical labs could make it more balanced.

Also, the interface has changed compared to the videos, so I used a lot the forum to help me find a workaround. I am very thankful and happy with the responsiveness on the forum.

The first two modules were good. There is a lot of useful information, difficult, but step by step it's possible to understand. The Jupyter Notebook and GitHub labs were interesting and easy to understand and follow all the steps to solve the exercises. Thanks for authors!

But module 3 was not interesting. As for me, it was like a confusion. The quality of videos, audios and screen shorts were bad: with different voices in one video, with noise sometimes, the screenshot was hard to read. The quality of video about Watson Data Refinery was bad and was so difficult to understand. I had some technical problems with Watson Studio and spent a lot of time solving them. The labs were not interesting. Only the final exam was good.

The most frustrating part was accessing IBM Watson Studio. It was difficult. That could be streamlined and made clearer. The instructions were clear, but getting into Watson Studio took a lot of time and research as the sign-on procedure has changed. If I work with Watson long enough, I'll be familiar with how it operates. Watson Studios kept opening new tabs, so it made it hard to know where I was. Additionally, since it had bounced my credit cards, I had opened several emails in an attempt to gain access. When I did get in, I had to open a couple to see which one I was granted access to. GitHub was intuitive and much more user friendly.

I wish it had gone smoother. I was very interested in learning Watson Studio. .

+It was good to get an overview of all the tools and and technologies involved with the industry at the beginning. There are just so many out there that it's nice to have some context for what everything is so I don't drown in industry specific lingo while I'm learning.

The structuring of the course was a little confusing. In particular the Week 1 Module broke everything down by open-source, commercial, and cloud tools, but then each of those sections used the same categories of tools(data management tools, visualization, etc). I think it would have been more effective to break it down by category, and the note which tools were open-source, commercial, etc. As it stands, it felt jumpy and hard to keep track of.

Content is ok although course spends way too much time in listing available tools and showing how tools developed. Instead, course should focus solely in showing the characteristics of the tools . Practice excercises are great though... maybe a bit too simple. It would be great to add more excercises and reduce the history and context.

Also the exams ate terrible!!! you should consult an education master to improve this, many questions were of thing that were barely mentioned in the training or that are purposely written in a confusing way. In any case exams dont realy measure how much you understood, but how good you are to take notes and retrieve the right data from the your notes

The course is ok but materials need to be updated to reflect latest changes to IBM tools, so that students don't waste time trying to find things while setting up and accessing tools. Also, could help to include more info with realistic examples or a few interview blips with real data scientists on when they use one tool over another- real context. LIke, why use Jupyter Notebooks over Zeppelin or IBM environment - the course addresses these at high level but I think perspectives from real people in the field what they're really using, and how it solves their day to day issues and workflows is a needed addition to the course to make it more immediately useful.

Two very important things:

1. Several links are not working. Several examples are currently unavailable for students. Pages don't load, or an error message is shown.

2. The final exam asks for students to do things that are not included in the course. I understand that students may need to do some research on their own in order to complete the final exam. This is great. But when showing how to do markdown cells for example, you can add a link to an IBM tutorial on Jupyter Notebooks (I found one on the internet) saying: "The final exam may include extra content not directly given during this course. Follow this link for a complete tutoria." That would be great.

There's a huge area of opportunity here to improve the content of the lessons, or most likely the way it is presented to the students. If you visit forums of week 1 and 2, you'll find a lot of discontent from the students. Even though the lessons content is good, the way it's introduced to the students is not the best. I'm only giving three stars to create awareness of this particular issue. In my personal opinion the course was ok, I understand it's just a glance of the several tools one can use for data science but if you're curious enough you can take enough advantage of the course's content. Great tools presented and a lot of self learning left to do.

There is plenty of information provided in the videos and as an absolute new person to data science I find the background information and tutorials helpful. The things I dislike are:

1 the quizzes are just finding facts / statements word for word from the videos.

2 there is little to no help on the discussion board and there is no one to assist you on the ibm watson prompts

3 the final quiz was graded our coding ability yet we never learned how to code in this class

4 most dislike the fact that our final assignment is peer reviewed by people are also in the course and not professionals. I dislike how my grade can be impacted by the mood of someone.

The flow is erratic, lot of random information not sure where to put it .. need to explain the process of what happens with data and why the apps are needed and what they do and how they help the process (work together) .. I heard about dozens of apps but have no clue when and if I would use them or why they are needed. I am following the course but mostly having to research what you are saying .. the language is high level or sometimes overly complicated. I only bring this up because this course said no prior experience needed there is not way a person having no IT experience will come away from these first 2 modules with an understanding

The Course was good but it could have been much better if it was based on Video tutorials. It was really difficult to understand the written instrustions for me initially as in to how to perform some of the codes which we had to execute just by reading the insturctions given in the course readings. I had to refer some of the videos from youtube to understant some of the instructions, so had been the course explained through video tutorials it would have been much better to understand the module. The module was good it was just that the study material was a bit tricky and time consuming to understand due to the absence of video explations.

I cannot give 5 stars because of the not updated materials. The IBM Watson Studio interface was completely different from the one explained in the videos and many of us had difficulties with it. Please, understand that for beginners like us, it was confusing and frustrating.

But, Technical Staff are very active in the forum and try to answer all our questions. Thank you for your help.

I have still a few concepts a bit unclear and I couldn't find the answers on the internet. My doubts are around the concepts: kernel, language and interpreter. Are they all synonims? What are the differences between them?

Hope my feedback is useful.

The course design is such that it covers a lot of breadth but very little depth. A plethora of tools are introduced but not much hands on time spent in the form of exercises (within the given four weeks). This was causing a lot of confusion, which was getting worse because of the many instructors with very short videos..

It would have been more effective if one person was to take the student thru all the topics and in longer video clips that these short 2 minutes to 10 minute.

Subject is interesting, tools are interesting, could have made the delivery process more interesting..

The labs were really helpful towards teaching you the skills. I feel that the videos could have been organized better, and that the unfamiliar terms could have been defined. I would recommend mid-video or end-of-video quizzes that test the content in the videos because I was unprepared for many of the questions in the practice quizzes even as an avid notetaker (similar to what is done in course 1 of the Introduction to Data Science Specialization). The end-of-course project is comprehensive and tests a lot of what is taught throughout the course.

Overall the course was informative. The issue is with the legitimacy of how the final project is peer graded. My first submission showed that it had failed. When I went through the comments/ criteria that someone graded me on I could tell that they just randomely clicked points. I re-submitted the same exact assignment and got a 100%. This shows me that there is no controls in place to catch people who do not take this seriously and just click boxes to grade someone quickly without actually looking at what that perosn wrote. That is disturbing.

The course introduced some interesting things especially Zeppelin notebooks which I wish I had encountered earlier. There were two main negatives to the course: 1) The videos and readings for IBM Watson Studio need some serious updating - once the account was created it was very trial and error to create a notebook. 2) Some terminology may be foreign to a beginner level student. As an example, the term "Spark" would be confusing to someone new to data science as there was little to no introduction to what it meant.

It felt like it was too soon to be going over so many programs and tools, when we haven't even learned how to do any part of the data science process yet in the certificate. It almost felt like a giant marketing ad for IBM. The labs were somewhat interesting. The week 4 instructions for how to do the final project were infuriating. It was so unclear what needed to be done, and you split it up into 4 sections when they all were essentially saying the same thing, yet also contradicting each other. Very frustrating.

Definitely not one of my best experiences in Coursera. Most of the transcripts need serious rework. The IBM badge is promised, but not granted; I will have to contact support. Some of the lecturers are hard to enjoy due to strong accent. There are several inconsistencies in the labs. 3 ungraded external tool sections are labelled with "This is new content that will be released in coming weeks and is not part of the current curriculum". In video "Data Refinery" the audio is partially very bad.

As a complete beginner, I felt at times, that the lecturer was speaking to a more experienced audience. They often referenced to the various languages, however this is not covered yet and would only sound familiar to people with previous coding experience.

I had a hard time finding the access to the tools I needed, in particular to create a project in Watson Studio.

Some of the videos are of older versions of the tools, therefore it took a bit of time to navigate the new layouts of the tools.

This course is definitely packed with tons of information, and I learned a lot, but it really felt like it was like trying to learn to drive by reading the car's owners manual. They introduce the expansive languages and tools available to data scientists, but it lacks any practical context or too much hands on experience actually using the massive amount of functionalities that are presented. With that said, it is more of a pure rote memory type course rather than a practical, hands-on one.

The course wasn't that bad. The only problem was that it was quite dry. Even with the practical labs, I didn't really have THAT much fun. As for what I learned, the course is basically there to give you an idea of the different Data Science tools that are used throughout the world today, and I like that just because this course is offered by IBM, they still didn't only tell us about tools created by them, but also open source tools and even commercial tools made by other companies.

Very light overview of the tools. Does not go beyond merely showing what the tools are, very little in-depth discussion of what the tools actually do, or what the differences are between them. The entire Waston website and such has changed since this course was first prepared, and it's a bit confusing to work around. As very light intro to the topic and tools, the course was ok, but probably not worth it for someone looking to really learn the tools and be challenged.